class_docstr: Implements an ML preprocessing operation.
  
  Preprocessors are stateful objects that can be fitted against a Dataset and used
  to transform both local data batches and distributed data. For example, a
  Normalization preprocessor may calculate the mean and stdev of a field during
  fitting, and uses these attributes to implement its normalization transform.
  
  Preprocessors can also be stateless and transform data without needed to be fitted.
  For example, a preprocessor may simply remove a column, which does not require
  any state to be fitted.
  
  If you are implementing your own Preprocessor sub-class, you should override the
  following:
  
  * ``_fit`` if your preprocessor is stateful. Otherwise, set
    ``_is_fittable=False``.
  * ``_transform_pandas`` and/or ``_transform_numpy`` for best performance,
    implement both. Otherwise, the data will be converted to the match the
    implemented method.
docstr: Run the transformation on a data batch in a NumPy ndarray format.
docstr_tok: ["Run","the","transformation","on","a","data","batch","in","a","NumPy","ndarray","format","."]
code: def _transform_numpy(
      self, np_data: Union["np.ndarray", Dict[str, "np.ndarray"]]
  ) -> Union["np.ndarray", Dict[str, "np.ndarray"]]:
      """Run the transformation on a data batch in a NumPy ndarray format."""
      raise NotImplementedError()
  
imports: import abc
  import base64
  import collections
  import pickle
  import warnings
  from enum import Enum
  from typing import TYPE_CHECKING, Any, Dict, Optional, Union
  from ray.air.util.data_batch_conversion import BatchFormat
  from ray.util.annotations import Deprecated, DeveloperAPI, PublicAPI
label_desc: Use image node_id 14 for calling the Preprocessor obj's underlying member method code with example usage: obj._transform_numpy(np_data) without return types